Mathew Vis-Dunbar
November 2023
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
This involves abstraction – the data and patterns may be represented as shapes (form), categorized with colours, and connected via position.
Even though an object as a whole might take some conscious effort to identify, the basic visual attributes that combine to make up that object are perceived without any conscious effort.
Stephen Few (2004). Tapping the Power of Visual Perception.
The things we process before we’re truly cognizant of the information.
The things we process before we’re truly cognizant of the information.
Form.
The things we process before we’re truly cognizant of the information.
Form. Colour.
The things we process before we’re truly cognizant of the information.
Form. Colour. Position.
The things we process before we’re truly cognizant of the information.
Form. Colour. Position.
Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication.
Stephen Few. Data Visualization for Human Perception.
Data are generally summarized using test statistics; a single number to describe an entire data set.
Averages means describing central tendencies
Spreads standard deviations describing the average spread of the data
Relationships correlation coefficients describing the direction of a relationship
| dataset | mean_x | mean_y | sd_x | sd_y | correlation |
|---|---|---|---|---|---|
| away | 54.26610 | 47.83472 | 16.76983 | 26.93974 | -0.0641284 |
| bullseye | 54.26873 | 47.83082 | 16.76924 | 26.93573 | -0.0685864 |
| circle | 54.26732 | 47.83772 | 16.76001 | 26.93004 | -0.0683434 |
| dino | 54.26327 | 47.83225 | 16.76514 | 26.93540 | -0.0644719 |
| dots | 54.26030 | 47.83983 | 16.76774 | 26.93019 | -0.0603414 |
| h_lines | 54.26144 | 47.83025 | 16.76590 | 26.93988 | -0.0617148 |
| high_lines | 54.26881 | 47.83545 | 16.76670 | 26.94000 | -0.0685042 |
| slant_down | 54.26785 | 47.83590 | 16.76676 | 26.93610 | -0.0689797 |
| slant_up | 54.26588 | 47.83150 | 16.76885 | 26.93861 | -0.0686092 |
| star | 54.26734 | 47.83955 | 16.76896 | 26.93027 | -0.0629611 |
| v_lines | 54.26993 | 47.83699 | 16.76996 | 26.93768 | -0.0694456 |
| wide_lines | 54.26692 | 47.83160 | 16.77000 | 26.93790 | -0.0665752 |
| x_shape | 54.26015 | 47.83972 | 16.76996 | 26.93000 | -0.0655833 |
Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17. https://doi.org/10.2307/2682899 Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 1290–1294. https://doi.org/10.1145/3025453.3025912
Counts, Distributions & Comparing Variables
Abstracting Further from the Data
Source: https://junkcharts.typepad.com/junk_charts/2013/05/more-power-brings-more-responsibility.html
Sequential
Diverging
Qualitative